Denotational Semantics for ODRL: Knowledge-Based Constraint Conflict Detection

2026-02-23

AI Summary

This work addresses the challenge that ODRL policy constraints rely on external domain knowledge, making effective cross-data-space conflict detection infeasible when such knowledge is absent. To overcome this limitation, the paper proposes the first knowledge base–grounded referential semantic framework that maps ODRL constraints into concept sets within three semantic domains: classification, part-whole, and nominal. The framework supports all ODRL composition patterns and enables safe-degraded conflict detection through a three-valued judgment—conflict, compatibility, or unknown. Formally grounded in the decidable EPR fragment of first-order logic, the approach is verified using Vampire and Z3. Evaluation across 154 benchmarks spanning six knowledge bases and four adversarial scenarios demonstrates consistent performance, revealing that the xone combinator requires stronger axioms and that exclusivity cannot generally hold under open-world assumptions.

Technology Category

• Knowledge Representation and Reasoning: Description Logics
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

ODRL's six set-based operators -- isA, isPartOf, hasPart, isAnyOf, isAllOf, isNoneOf -- depend on external domain knowledge that the W3C specification leaves unspecified. Without it, every cross-dataspace policy comparison defaults to Unknown. We present a denotational semantics that maps each ODRL constraint to the set of knowledge-base concepts satisfying it. Conflict detection reduces to denotation intersection under a three-valued verdict -- Conflict, Compatible, or Unknown -- that is sound under incomplete knowledge. The framework covers all three ODRL composition modes (and, or, xone) and all three semantic domains arising in practice: taxonomic (class subsumption), mereological (part-whole containment), and nominal (identity). For cross-dataspace interoperability, we define order-preserving alignments between knowledge bases and prove two guarantees: conflicts are preserved across different KB standards, and unmapped concepts degrade gracefully to Unknown -- never to false conflicts. A runtime soundness theorem ensures that design-time verdicts hold for all execution contexts. The encoding stays within the decidable EPR fragment of first-order logic. We validate it with 154 benchmarks across six knowledge base families (GeoNames, ISO 3166, W3C DPV, a GDPR-derived taxonomy, BCP 47, and ISO 639-3) and four structural KBs targeting adversarial edge cases. Both the Vampire theorem prover and the Z3 SMT solver agree on all 154 verdicts. A key finding is that exclusive composition (xone) requires strictly stronger KB axioms than conjunction or disjunction: open-world semantics blocks exclusivity even when positive evidence appears to satisfy exactly one branch.

Problem

• ODRL
• constraint conflict detection
• denotational semantics
• knowledge-based reasoning
• cross-dataspace interoperability

Innovation

• Denotational Semantics
• ODRL
• Conflict Detection
• Knowledge Base Alignment
• EPR Fragment